{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Mistral's Codestral with code interpreting and analyzing dataset\n",
        "**Powered by open-source [Code Interpreter SDK](https://github.com/e2b-dev/code-interpreter) by [E2B](https://e2b.dev/docs)**\n",
        "\n",
        "Read more about Mistral's new Codestral model [here](https://mistral.ai/news/codestral/).\n",
        "\n",
        "E2B's code interpreter SDK quickly creates a secure cloud sandbox powered by [Firecracker](https://github.com/firecracker-microvm/firecracker). Inside this sandbox is a running Jupyter server that the LLM can use."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Step 1: Install dependencies\n",
        "\n",
        "We start by installing the [E2B code interpreter SDK](https://github.com/e2b-dev/code-interpreter) and [Mistral's Python SDK](https://console.mistral.ai/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsUPOWJl5pn9",
        "outputId": "e459be0c-d698-4b99-cde4-480b8a5e42d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install mistralai==0.4.2 e2b_code_interpreter==1.0.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Step 2: Define API keys and prompt\n",
        "\n",
        "Let's define our variables with API keys for Mistral and E2B together with the model ID and prompt. We won't be defining any tools because Codestral doesn't support tool usage yet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "HnxngrHnWlV8"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from dotenv import load_dotenv\n",
        "load_dotenv()\n",
        "\n",
        "# TODO: Get your Mistral API key from https://console.mistral.ai\n",
        "MISTRAL_API_KEY = os.getenv(\"MISTRAL_API_KEY\")\n",
        "\n",
        "# TODO: Get your E2B API key from https://e2b.dev/docs\n",
        "E2B_API_KEY = os.getenv(\"E2B_API_KEY\")\n",
        "\n",
        "MODEL_NAME = \"codestral-latest\"\n",
        "\n",
        "SYSTEM_PROMPT = \"\"\"You're a python data scientist that is analyzing daily temperature of major cities. You are given tasks to complete and you run Python code to solve them.\n",
        "\n",
        "Information about the temperature dataset:\n",
        "- It's in the `/home/user/city_temperature.csv` file\n",
        "- The CSV file is using `,` as the delimiter\n",
        "- It has following columns (examples included):\n",
        "  - `Region`: \"North America\", \"Europe\"\n",
        "  - `Country`: \"Iceland\"\n",
        "  - `State`: for example \"Texas\" but can also be null\n",
        "  - `City`: \"Prague\"\n",
        "  - `Month`: \"June\"\n",
        "  - `Day`: 1-31\n",
        "  - `Year`: 2002\n",
        "  - `AvgTemperature`: temperature in Celsius, for example 24\n",
        "\n",
        "Generally, you follow these rules:\n",
        "- ALWAYS FORMAT YOUR RESPONSE IN MARKDOWN\n",
        "- ALWAYS RESPOND ONLY WITH CODE IN CODE BLOCK LIKE THIS:\n",
        "```python\n",
        "{code}\n",
        "```\n",
        "- the python code runs in jupyter notebook.\n",
        "- every time you generate python, the code is executed in a separate cell. it's okay to multiple calls to `execute_python`.\n",
        "- display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.\n",
        "- you have access to the internet and can make api requests.\n",
        "- you also have access to the filesystem and can read/write files.\n",
        "- you can install any pip package (if it exists) if you need to be running `!pip install {package}`. The usual packages for data analysis are already preinstalled though.\n",
        "- you can run any python code you want, everything is running in a secure sandbox environment\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a74aGJUdjonY"
      },
      "source": [
        "Because Codestral doesn't support function calling yet, we instruct the model to return messages in Markdown and then parse and extract the Python code block on our own."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "yVwSBPF1iHby"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "pattern = re.compile(r'```python\\n(.*?)\\n```', re.DOTALL) # Match everything in between ```python and ```\n",
        "def match_code_block(llm_response):\n",
        "  match = pattern.search(llm_response)\n",
        "  if match:\n",
        "    code = match.group(1)\n",
        "    print(code)\n",
        "    return code\n",
        "  return \"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Step 3: Implement the method for code interpreting\n",
        "\n",
        "Here's the main function that uses the E2B code interpreter SDK. We'll be calling this function a little bit further when we're parsing the Codestral's response with tool calls."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "knZ-_2qtXkqM"
      },
      "outputs": [],
      "source": [
        "def code_interpret(e2b_code_interpreter, code):\n",
        "  print(\"Running code interpreter...\")\n",
        "  exec = e2b_code_interpreter.run_code(code,\n",
        "  on_stderr=lambda stderr: print(\"[Code Interpreter]\", stderr),\n",
        "  on_stdout=lambda stdout: print(\"[Code Interpreter]\", stdout))\n",
        "\n",
        "  if exec.error:\n",
        "    print(\"[Code Interpreter ERROR]\", exec.error)\n",
        "  else:\n",
        "    return exec.results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Step 4: Implement the method for calling Codestral and parsing its response\n",
        "\n",
        "Now we're going to define and implement `chat` method. In this method, we'll call the Codestral LLM, parse the output to extract any Python code block, and call our `code_interpret` method we defined above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "udfl9vKoXnBn"
      },
      "outputs": [],
      "source": [
        "from mistralai.client import MistralClient\n",
        "\n",
        "client = MistralClient(api_key=MISTRAL_API_KEY)\n",
        "\n",
        "def chat(e2b_code_interpreter, user_message):\n",
        "  print(f\"\\n{'='*50}\\nUser message: {user_message}\\n{'='*50}\")\n",
        "\n",
        "  messages = [\n",
        "      {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
        "      {\"role\": \"user\", \"content\": user_message}\n",
        "  ]\n",
        "\n",
        "  # Codestral doesn't support tools/function calling yet\n",
        "  response = client.chat(\n",
        "      model=MODEL_NAME,\n",
        "      messages=messages,\n",
        "  )\n",
        "  response_message = response.choices[0].message\n",
        "  python_code = match_code_block(response_message.content)\n",
        "  if python_code != \"\":\n",
        "    code_interpreter_results = code_interpret(e2b_code_interpreter, python_code)\n",
        "    return code_interpreter_results\n",
        "  else:\n",
        "    print(f\"Failed to match any Python code in model's response {response_message}\")\n",
        "    return[]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4-9Vj3lkiB1"
      },
      "source": [
        "### Step 5: Implement method for uploading dataset to code interpreter sandbox\n",
        "\n",
        "The file gets uploaded to the E2B sandbox where our code interpreter is running. We get the file's remote path in the `remote_path` variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "29lU13cQkg36"
      },
      "outputs": [],
      "source": [
        "def upload_dataset(code_interpreter):\n",
        "  print(\"Uploading dataset to Code Interpreter sandbox...\")\n",
        "  file_path = \"./city_temperature.csv\"\n",
        "  with open(file_path, \"rb\") as f:\n",
        "    code_interpreter.files.write(file_path, f)\n",
        "  print(\"Uploaded at\", file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zX90GEiPkrOX"
      },
      "source": [
        "### Step 6: Put everything together\n",
        "\n",
        "In this last step, we put all the pieces together. We instantiate a new code interpreter instance using\n",
        "\n",
        "```py\n",
        "with CodeInterpreter(api_key=E2B_API_KEY) as code_interpreter:\n",
        "```\n",
        "\n",
        "and then call the `chat` method with our user message and the `code_interpreter` instance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LIs_hCRlYD-X",
        "outputId": "cad8a089-389e-4774-88d7-79f28ef39aa0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Uploading dataset to Code Interpreter sandbox...\n",
            "Uploaded at /home/user/city_temperature.csv\n",
            "\n",
            "==================================================\n",
            "User message: Plot average temperature over the years in Algeria\n",
            "==================================================\n",
            "import pandas as pd\n",
            "import matplotlib.pyplot as plt\n",
            "\n",
            "# Load the data\n",
            "df = pd.read_csv('/home/user/city_temperature.csv')\n",
            "\n",
            "# Filter the data for Algeria\n",
            "df_algeria = df[df['Country'] == 'Algeria']\n",
            "\n",
            "# Group the data by year and calculate the average temperature\n",
            "df_algeria_yearly = df_algeria.groupby('Year')['AvgTemperature'].mean()\n",
            "\n",
            "# Plot the data\n",
            "plt.figure(figsize=(10, 6))\n",
            "plt.plot(df_algeria_yearly.index, df_algeria_yearly.values)\n",
            "plt.title('Average Temperature Over the Years in Algeria')\n",
            "plt.xlabel('Year')\n",
            "plt.ylabel('Average Temperature (Celsius)')\n",
            "plt.show()\n",
            "Running code interpreter...\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<e2b_code_interpreter.models.Result at 0x1380a5210>"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from e2b_code_interpreter import Sandbox\n",
        "\n",
        "with Sandbox(api_key=E2B_API_KEY) as code_interpreter:\n",
        "  # Upload the dataset to the code interpreter sandbox\n",
        "  upload_dataset(code_interpreter)\n",
        "\n",
        "  code_results = chat(\n",
        "    code_interpreter,\n",
        "    \"Plot average temperature over the years in Algeria\"\n",
        "  )\n",
        "  if code_results:\n",
        "    first_result = code_results[0]\n",
        "  else:\n",
        "    raise Exception(\"No code interpreter results\")\n",
        "\n",
        "\n",
        "# This will render the image\n",
        "# You can also access the data directly\n",
        "# first_result.png\n",
        "# first_result.jpg\n",
        "# first_result.pdf\n",
        "# ...\n",
        "first_result"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
